Shaobo Lin

发表

Is Extreme Learning Machine Feasible? A Theoretical Assessment (Part I)

Xia Liu, Zongben Xu, Jian Fang, 2015, IEEE Transactions on Neural Networks and Learning Systems.

Almost optimal estimates for approximation and learning by radial basis function networks

Xia Liu, Zongben Xu, Shaobo Lin, 2013, Machine Learning.

Global Convergence of Block Coordinate Descent in Deep Learning

Yuan Yao, Jinshan Zeng, Shaobo Lin, 2018, ICML.

Constructive approximate interpolation by neural networks in the metric space

Zongben Xu, Feilong Cao, Shaobo Lin, 2010, Math. Comput. Model..

Learning Through Deterministic Assignment of Hidden Parameters

Zongben Xu, Jian Fang, Shaobo Lin, 2018, IEEE Transactions on Cybernetics.

Fast Learning With Polynomial Kernels

Jinshan Zeng, Shaobo Lin, Shaobo Lin, 2019, IEEE Transactions on Cybernetics.

Essential rate for approximation by spherical neural networks

Zongben Xu, Feilong Cao, Shaobo Lin, 2011, Neural Networks.

Re-scale boosting for regression and classification pdf

Yao Wang, Lin Xu, Shaobo Lin, 2015, ArXiv.

Constructive Neural Network Learning

Xiaoqin Zhang, Jinshan Zeng, Shaobo Lin, 2016, IEEE Transactions on Cybernetics.

Block Coordinate Descent for Deep Learning: Unified Convergence Guarantees pdf

Yuan Yao, Jinshan Zeng, Shaobo Lin, 2018, ArXiv.

Is Extreme Learning Machine Feasible? A Theoretical Assessment (Part I)

Xia Liu, Zongben Xu, Jian Fang, 2015, IEEE Trans. Neural Networks Learn. Syst..

A Convergence Analysis of Nonlinearly Constrained ADMM in Deep Learning pdf

Yuan Yao, Jinshan Zeng, Shaobo Lin, 2019, ArXiv.

Does generalization performance of lq regularization learning depend on q? A negative example pdf

Chen Xu, Jinshan Zeng, Jian Fang, 2013, ArXiv.

Divide and Conquer Local Average Regression pdf

Yao Wang, Xiangyu Chang, Shaobo Lin, 2016, ArXiv.

GAITA: A Gauss-Seidel iterative thresholding algorithm for ℓq regularized least squares regression

Jinshan Zeng, Zhimin Peng, Shaobo Lin, 2017, J. Comput. Appl. Math..

Model selection of polynomial kernel regression pdf

Zongben Xu, Jinshan Zeng, Xingping Sun, 2015, ArXiv.

Shrinkage Degree in $L_{2}$ -Rescale Boosting for Regression

Zongben Xu, Yao Wang, Shaobo Lin, 2015, IEEE Transactions on Neural Networks and Learning Systems.

Error Estimate for Spherical Neural Networks Interpolation

Zongben Xu, Jinshan Zeng, Shaobo Lin, 2015, Neural Processing Letters.

Jackson-type inequalities for spherical neural networks with doubling weights

Zongben Xu, Jinshan Zeng, Lin Xu, 2015, Neural Networks.

Linear and nonlinear approximation of spherical radial basis function networks

Shaobo Lin, 2016, J. Complex..

Greedy metrics in orthogonal greedy learning pdf

Zongben Xu, Jinshan Zeng, Lin Xu, 2014, ArXiv.

Simultaneous approximation by spherical neural networks

Feilong Cao, Shaobo Lin, Shaobo Lin, 2016, Neurocomputing.

Sparse solution of underdetermined linear equations via adaptively iterative thresholding pdf

Zongben Xu, Jinshan Zeng, Shaobo Lin, 2013, Signal Process..

Learning Rates of lq Coefficient Regularization Learning with Gaussian Kernel

Zongben Xu, Jinshan Zeng, Jian Fang, 2013, Neural Computation.

Regularization: Convergence of Iterative Half Thresholding Algorithm

Zongben Xu, Jinshan Zeng, Yao Wang, 2014 .

Shrinkage degree in L2-re-scale boosting for regression pdf

Zongben Xu, Lin Xu, Yao Wang, 2015, ArXiv.

Distributed Semi-supervised Learning with Kernel Ridge Regression

Xiangyu Chang, Ding-Xuan Zhou, Shaobo Lin, 2017, J. Mach. Learn. Res..

Nonparametric regression using needlet kernels for spherical data pdf

Shaobo Lin, Shaobo Lin, 2015, J. Complex..

Learning and approximation capabilities of orthogonal super greedy algorithm pdf

Zongben Xu, Jian Fang, Shaobo Lin, 2014, Knowl. Based Syst..

Corrigendum to "GAITA: A Gauss-Seidel iterative thresholding algorithm for lq regularized least squares regression" [J. Comput. Appl. Math. 319 (2017) 220-235]

Jinshan Zeng, Zhimin Peng, Shaobo Lin, 2018, J. Comput. Appl. Math..

Distributed Learning with Regularized Least Squares pdf

Ding-Xuan Zhou, Shaobo Lin, Xin Guo, 2016, J. Mach. Learn. Res..

Sparse Regularization: Convergence Of Iterative Jumping Thresholding Algorithm

Zongben Xu, Jinshan Zeng, Shaobo Lin, 2014, IEEE Transactions on Signal Processing.

Greedy Criterion in Orthogonal Greedy Learning

Xia Liu, Yi Fang, Zongben Xu, 2016, IEEE Transactions on Cybernetics.

Learning Capability of Relaxed Greedy Algorithms

Zongben Xu, Xingping Sun, Shaobo Lin, 2013, IEEE Transactions on Neural Networks and Learning Systems.

A Gauss-Seidel Iterative Thresholding Algorithm for lq Regularized Least Squares Regression pdf

Jinshan Zeng, Zhimin Peng, Shaobo Lin, 2015, ArXiv.

Learning Rates for Classification with Gaussian Kernels

Jinshan Zeng, Xiangyu Chang, Shaobo Lin, 2017, Neural Computation.

Unified Low-Rank Matrix Estimate via Penalized Matrix Least Squares Approximation

Xiangyu Chang, Yao Wang, Shaobo Lin, 2019, IEEE Transactions on Neural Networks and Learning Systems.

Learning capability of the truncated greedy algorithm

Zongben Xu, Lin Xu, Shaobo Lin, 2016, Science China Information Sciences.

Construction of Neural Networks for Realization of Localized Deep Learning pdf

Charles K. Chui, Ding-Xuan Zhou, Shaobo Lin, 2018, Front. Appl. Math. Stat..

Approximation capability of interpolation neural networks

Zongben Xu, Feilong Cao, Shaobo Lin, 2010, Neurocomputing.

A general radial quasi-interpolation operator on the sphere

Zongben Xu, Feilong Cao, Xiangyu Chang, 2012, J. Approx. Theory.

A maximum power point tracking method based on tabu search for PV systems under partially shaded conditions

Chun Wei, Shaobo Lin, Yifei Zheng, 2011 .

Approximation by neural networks with scattered data

Zongben Xu, Feilong Cao, Shaobo Lin, 2013, Appl. Math. Comput..

Deep Neural Network Based Vehicle and Pedestrian Detection for Autonomous Driving: A Survey

Hangbin Wu, Dongpu Cao, Shaobo Lin, 2021, IEEE Transactions on Intelligent Transportation Systems.

The estimate for approximation error of spherical neural networks

Feilong Cao, Shaobo Lin, Huazhong Wang, 2011 .

Multivariate Jackson-type inequality for a new type neural network approximation

Shaobo Lin, Yuanhua Rong, Zongben Xu, 2014 .

Interpolation and Best Approximation for Spherical Radial Basis Function Networks

Jinshan Zeng, Shaobo Lin, Zongben Xu, 2013 .

Distributed Kernel-Based Gradient Descent Algorithms

Ding-Xuan Zhou, Shaobo Lin, Ding-Xuan Zhou, 2018 .

L p Error Estimates for Scattered Data Interpolation On Spheres

Feilong Cao, Shaobo Lin, Shaobo Lin, 2011 .

The capability of approximation for neural networks interpolant on the sphere

Feilong Cao, Shaobo Lin, Shaobo Lin, 2010 .

The essential rate of approximation for radial function manifold

Feilong Cao, Shaobo Lin, Zongben Xu, 2011 .